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Learn to develop reliable data processing systems. Join today to learn from an expert Google Data engineer.

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Course Duration

30 Hrs.

Live Project

2 Project

Certification Pass

Guaranteed

Training Format

Live Online /Self-Paced/Classroom

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Google Cloud

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CURRICULUM & PROJECTS

Google Professional Data Cloud Engineer Training Program

    Data processing Fundamentals

    • Data Processing Concepts
    • Data Processing Pipelines

    Data Storage Fundamentals

    • About GCP
    • Data Storage in GCP
    • Working with Data
    • Cloud Storage
    • Data Transfer Services
    • Cloud Fire Store
    • Cloud Spanner
    • Cloud Memory Store
    • Different Memory options

    Selecting the best memory storage

    • Compare storage options
    • Mapping storage systems to business requirements
    • Data modeling
    • Trade-offs involving latency, throughput, transactions
    • Distributed systems
    • Schema design
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    Data publishing and visualization

    Online (interactive) vs. batch predictions

    Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)

    Big Data Ecosystem

    • MapReduce
    • Hadoop & HDFS
    • Apache Pig
    • Apache Spark
    • Apache Kafka

    Real-time Messaging with Pub/Sub

    • Pub/sub basics
    • pub/Sub Terminologies
    • Advanced Pub/Sub Concepts
    • Working with Pub/Sub

    Cloud Data Flow Pipelining

    • Introduction to Data flow
    • Pipeline Lifecycle
    • Dataflow pipeline concepts
    • Advanced Dataflow concepts
    • Dataflow security and access
    • Using Dataflow

    Cloud Dataproc

    • Dataproc Basics
    • Working with Dataproc
    • Advanced Dataproc

    NoSQL Data with Cloud Big Table

    • Big Table Concepts
    • Big Table Architecture
    • Big Table Data Model
    • Big Table Schema Design
    • Big Table Advanced Concepts

    Data Analytics using BigQuery

    • BigQuery Basics
    • Using BigQuery
    • Partitioning and Clustering
    • Best Practices
    • Securing BigQuery
    • BigQuery Monitoring and Logging
    • Machine Learning with BigQuery ML
    • Working with BigQuery
    • Advanced BigQuery Concepts

    Data Exploration with Cloud Datalab

    • Datalab Concepts
    • Working with Datalab

    Visualization with Cloud Data Studio

    • Reporting & Business intelligence
    • Data Distribution
    • Introduction to Cloud Data Studio
    • Charts and Filters

    Job automation and orchestration (e.g., Cloud Composer)

    • Orchestration with Cloud Composer
    • Cloud Composer Overview
    • Cloud Composer Architecture
    • Working with Cloud Composer
    • Advanced Cloud Composer Concepts
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    Steps for Designing

    • Choice of infrastructure
    • System availability and fault tolerance
    • Use of distributed systems
    • Capacity planning
    • Hybrid cloud and edge computing
    • Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
    • At least once, in-order, and exactly once, etc., event processing

    Migrating data warehousing and data processing

    • Awareness of current state and how to migrate a design to a future state
    • Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
    • Validating a migration
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    Building and operationalizing Storage Solutions

    • Cloud Managed Services
    • Effectives Use of Managed Services
    • Storage Cost and performance
    • Lifecycle Management of Data

    Building and operationalizing Pipelines

    • Data cleansing
    • Batch and streaming
    • Transformation
    • Data acquisition and import
    • Integrating with new data sources

    Building and operationalizing processing infrastructure

    • Provisioning resources
    • Monitoring pipelines
    • Adjusting pipelines
    • Testing and quality control
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    Introduction to Machine Learning

    • Machine Learning Introduction
    • Machine Learning Basics
    • Machine Learning Types and Models
    • Overfitting
    • Hyperparameters
    • Feature Engineering

    Machine Learning with TesnorFlow

    • Deep Learning with TensorFlow
    • Introduction to Artificial Neural Networks
    • Neural Network Architectures
    • Building a Neural Network

    Leveraging pre-built ML models as a service. Considerations include:

    • ML APIs (e.g., Vision API, Speech API)
    • Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
    • Conversational experiences (e.g., Dialogflow)

    Deploying an ML pipeline

    • Ingesting appropriate data
    • Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
    • Continuous evaluation

    Choosing the appropriate training and serving infrastructure

    • Distributed vs. single machine
    • Use of edge compute
    • Hardware accelerators (e.g., GPU, TPU)

    Measuring, monitoring, and troubleshooting machine learning models

    • Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
    • Impact of dependencies of machine learning models
    • Common sources of error (e.g., assumptions about data)
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    Designing for security and compliance

    • Identity and access management (e.g., Cloud IAM)
    • Data security (encryption, key management)
    • Ensuring privacy (e.g., Data Loss Prevention API)
    • Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))

    Ensuring scalability and efficiency

    • Building and running test suites
    • Pipeline monitoring (e.g., Stack Driver)
    • Assessing, troubleshooting, and improving data representations and data processing infrastructure
    • Resizing and autoscaling resources

    Ensuring reliability and fidelity

    • Performing data preparation and quality control (e.g., Cloud Dataprep)
    • Verification and monitoring
    • Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
    • Choosing between ACID, idempotent, eventually consistent requirements

    Ensuring flexibility and portability

    • Mapping to current and future business requirements
    • Designing for data and application portability (e.g., multi-cloud, data residency requirements)
    • Data staging, catalog, and discovery
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SELF ASSESSMENT

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Google Professional Data Cloud Engineer Training Program

Category Associate
Exam Name: Google Cloud – Professional Data Engineer
Exam Code: PDE
Exam Duration: 120 Mins
Exam Format: Multiple Choice and Multiple Select Questions
Passing Score: 70% (Score not officially disclosed by Google)

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Mock Interviews

Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures.Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.
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WHAT OUR ALUMNI SAYS ABOUT US

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Students Placements & Reviews

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FAQ's

It's a certification that validates your ability to design, build, manage, and secure data processing systems on Google Cloud.

This course is ideal for data engineers, cloud engineers, and professionals working with big data and analytics platforms.

You will learn data ingestion, processing, storage, machine learning, and how to optimize data pipelines on GCP.

Basic knowledge of cloud computing and experience with SQL or Python is helpful but not mandatory.

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  • - Get Tips from Trainer to Clear Interviews
  • - Attend Mock-Up Interviews with Experts
  • - Get Interviews & Get Hired

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